The Federal Aviation Administration's (FAA's), joint industry-government initiative—the Joint Program Development Office (JPDO)—is responsible for charting the Next Generation Air Transportation System (NextGen). One of the strategic objectives outlined in the JPDO's operational concept is to ensure that flight operator objectives are balanced with overall NAS performance objectives. To ensure that this objective is met a process called Flow Contingency Management (FCM) has been proposed. The FCM process aims to alleviate the demand capacity imbalance that could originate as a result of excessive demand for a particular airspace or reduced capacity because of operational constraints in a manner that is equitable across multiple stakeholders.
The FAA in its Operational Evolution Partnership (OEP) emphasizes the need for major improvement in collaborative air traffic management (CATM) process. OEP highlights that NextGen CATM philosophy should be driven to accommodate flight operator preferences to the maximum extent possible and to impose restrictions only when a real operational need exists to meet the demand. Furthermore in case the constraints are required, the goal should be to maximize the operators' opportunities to resolve them based on their own preferences.
The OEP outlines that NextGen CATM system should be interactive and iterative and flight operators should be able to interact with a set of flow planning services to manage their operations. The flow planning services will provide a trajectory analysis capability so that flight plans can be mapped against the available resources for compatibility analysis. In addition, through the flow planning services, a common set of flow strategies will be shared with all the stakeholders to promote a common situational awareness of the NAS operating plan.
Steadily increasing traffic densities have motivated the use of automation to alleviate controller workload and increase sector capacities. The “Automated Airspace,” is described as a concept wherein automated flight separation command and control is proposed as a powerful means to decrease controller workload and thereby increase sector capacity. The role of aircraft-to-aircraft separation as a key traffic flow and congestion management control parameter has been highlighted. In current traffic flow management practice, aircraft-to-aircraft separation (miles-in-trail) is a widely used strategy for managing congestion and workload. There is limited capability to assess the consequences of these actions, and controllers must rely primarily on experience to assess if their miles-in-trail actions will have desired impacts on traffic flow demands. In response to this need a miles-in-trail impact assessment simulation system capability was developed by MITRE.
Traffic controllers work at the level of sectors. The aggregate-level consisting of several sectors is called a center. Efficient forecasting of traffic flows and congestion at the center-level is important to anticipate and adapt to changing situations. Simulation-based—such as the Reorganized Air Traffic Control Mathematical Simulator (RAMS Plus) gate-to-gate simulator—or model-based methods have therefore evolved to support this need. Control theoretic models that consider the impact of tactical air traffic control actions on traffic flows have also been developed. Such a model may be used to augment simulation-based methods. Simulation-based methods typically have the resources to include multiple specialized fine-grained and coarse-grained hybrid models, each for a given NAS resource, to assess the aggregate impact of traffic flow and air traffic control strategy performance, and therefore tend to be more realistic in assumptions and overall behavior.
Moderate to severe weather patterns have a principal effect on the efficiency of NAS operations. Due to the complex nature of the probabilistic influence of weather on traffic flows, simulation has been pursued as a method to assess system performance impacts. In current practice, rerouting around expected weather patterns is typically utilized as a principal traffic flow management strategy. In research carried out relating to stochasticity in traffic flow management, dynamic tactical reactive rerouting strategies for aircraft under probabilistic weather influence assumptions are considered. Longer-term anticipatory rerouting allows a greater degree of planning freedom than shorter-term reactive tactical rerouting. Given that efficient anticipatory rerouting requires reliable weather forecasts, and given significant inherent uncertainties in the weather forecasts themselves, efforts have been invested to accommodate and manage forecast variance in traffic flow decision-making.
A number of optimization-based planning methods and tools have been developed for traffic flow management. Airspace configurations and traffic patterns have a principal effect on controller workload and efficiency. An airspace sector aggregation or partitioning meta-heuristic algorithm for European skies having the potential to improve safety by reducing controller workload has been proposed. “Airspace Complexity” is a term that has been proposed to capture the influence that airspace configurations and traffic flow patterns have on controller workload and efficiency. However, this relationship is complex, and planning tools that operate in this environment must be able to accommodate nonlinearities, continuous and discrete variables, and high-dimensional search. Therefore, stochastic optimization methods such as evolutionary or genetic algorithms have been applied for planning and decision-support at multiple levels: at the sector configuration level; at the route and departure time planning levels through; and at the airport ground operations level.
Heuristic and mathematical programming-based techniques have also been proposed for solving several aspects of traffic flow management. In general though, mathematical programming approaches tend to make simplifying assumptions of the nature of the traffic flow behavior and management action options in order to accommodate solutions within tractable parametric search spaces. They also tend to work off a baseline simulation assessment, and do not include a realistic simulation in the optimization stage, as the problem formulation is used as a proxy for the airspace simulation. In addition, these techniques typically result in a single final solution, which if found unacceptable for any reason would necessitate computationally expensive solution regeneration.
The U.S. National Airspace accommodates over 50,000 flights daily. During an operational day, paths for upcoming flights within a time horizon are filed by the various Airline Operators (AOC) with the Air Traffic Control System Command Center (ATCSCC). Once the AOCs have generated a flight path option for a particular flight they submit it to the ATCSCC. However, since the AOC planning is done significantly in advance, and the predictability of weather is low much in advance of departure, there needs to be flexibility to manage uncertainty and meet AOC business objectives. Theoretically, an AOC can wait until the last minute to file the flight plan, but in practice an AOC has numerous flight plans to process, so they must continue to file flight plans in order to manage their workload. In case weather does not pose a problem the AOC should get the best possible route. In case weather does pose a problem the AOC should be able to settle for their second choice. So to respond to the inherent uncertainty, an AOC does the trial planning process iteratively and prepares a list of options that meets their goals. The AOC consequently files a flight plan that has multiple flight path options ranked in order of preference.